Linear regression is best used for which type of outcome?

Linear regression is best employed when the outcome variable is continuous. This means that the dependent variable can take on an infinite number of values within a given range, which enables linear regression to analyze the relationship between that continuous outcome and one or more predictor variables. For example, it can be used to predict a continuous outcome such as blood pressure, weight, or age based on independent variables.

In contrast, categorical outcomes refer to variables that represent distinct groups or categories, such as gender or race. These types of outcomes are typically analyzed using methods such as logistic regression, which is suited to situations where the outcome is not continuous but rather falls into categories. Similarly, binary outcomes are a specific type of categorical outcome that includes only two categories, like presence or absence of a condition. Qualitative outcomes, which reflect subjective assessments or classifications, also do not align with the requirements for linear regression.

Overall, the continuous nature of the outcome variable is what makes linear regression the ideal choice in this context, allowing for meaningful statistical relationships to be explored and quantified.